Qwen3.5-0.8B-heretic-GGUF
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探索 AI 模型 84997
Qwen3.5-0.8B-heretic-imatrix-GGUF
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查看详情 ↗Qwen3.5-2B-heretic-GGUF
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查看详情 ↗Qwen3.5-2B-heretic-imatrix-GGUF
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查看详情 ↗Qwen3.5-4B-heretic-GGUF
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查看详情 ↗Qwen3.5-4B-heretic-imatrix-GGUF
FadedRedStarThis repository hosts importance-matrix (imatrix) optimized GGUF weights, available in multiple quantization formats, and the associated vision projection matrix for **Qwen3.5-4B-heretic**, quantized from the source floating-point tensors provided by coder3101/Qwen3.5-4B-heretic. **🔄 Sister Repository:** Check out the Standard GGUF Sister Repository for uncalibrated and full 8-bit precision options. An **Importance Matrix (imatrix)** calculation tracks activations across network layers using a calibration sequence, then weights the quantization process to preserve the parameters that matter most for output quality — improving fidelity at low bit depths.
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查看详情 ↗Qwen3.5-9B-heretic-GGUF
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查看详情 ↗Qwen3.5-9B-heretic-imatrix-GGUF
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查看详情 ↗QwenPaw-Flash-9B-heretic-GGUF
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查看详情 ↗QwenPaw-Flash-9B-heretic-imatrix-GGUF
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Fadelseth基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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查看详情 ↗flowhmr-0.46B
fafsaf1基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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查看详情 ↗FahadPrimeX-b27-V3-GGUF
FahadPrimeX基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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查看详情 ↗TypoFormer
fahiiim基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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查看详情 ↗indobert_ner_p1
fahmisyaifudinThis repository contains a fine-tuned IndoBERT model for Indonesian Named Entity Recognition, wrapped in a HuggingFace-compatible pipeline for easy usage. - **PERSON** — Names of individual humans - **REPRESENTATIVE_PERSON** — Government/public roles - **POLITICAL_ORGANIZATION** — Constitutional political institutions - **ORGANIZATION** — Companies, universities, BUMN , NGOs - **LAW** — UU, UUD, Kepres - **EVENT** — National events, gov programs
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查看详情 ↗--- library_name: transformers license: other license_name: mnpl license_link: https://mistral.ai/licences/MNPL-0.1.md tags: - code language: - code --- This is mistralai/Codestral-22B-v0.1 with orthogonalized bfloat16 safetensor weights, generated with a refined methodology based on that which was described in the preview paper/blog post: 'Refusal in LLMs is mediated by a single direction' which I encourage you to read to understand more. TL;DR: This model has had certain weights manipulated to "inhibit" the model's ability to express refusal. It is not in anyway _guaranteed_ that it won't refuse you, understand your request, it may still lecture you about ethics/safety, etc. It is tuned in all other respects the same as the original 22B model was, just with the strongest refusal directions orthogonalized out.
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查看详情 ↗This is meta-llama/Llama-3-70B-Instruct with orthogonalized bfloat16 safetensor weights, generated with the methodology that was described in the preview paper/blog post: 'Refusal in LLMs is mediated by a single direction' which I encourage you to read to understand more. TL;DR: this model has had certain weights manipulated to "inhibit" the model's ability to express refusal. It is not in anyway _guaranteed_ that it won't refuse you, understand your request, it may still lecture you about ethics/safety, etc. It is tuned in all other respects the same as the original 70B instruct model was, just with the strongest refusal direction orthogonalized out. In the repo, I've included `refusal_dir.pth` If you have Llama-3-70B-Instruct model downloaded already, you can use the ortho cookbook to apply it to your downloaded model, which will make it the same as what you'd download from here.
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价格待核验多模态与渠道费用须分别核对
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价格待核验多模态与渠道费用须分别核对
查看详情 ↗This is meta-llama/Meta-Llama-3-70B-Instruct with orthogonalized bfloat16 safetensor weights, generated with a refined methodology based on that which was described in the preview paper/blog post: 'Refusal in LLMs is mediated by a single direction' which I encourage you to read to understand more. Second try. I felt that the V3 methodology of 70B wasn't well applied, and u/Nexesenex on reddit kinda confirmed my suspicions. So go blame them. :P This one has only a single layer modified(!) and that seems to have completely eliminated moralizing disclaimers.
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价格待核验多模态与渠道费用须分别核对
查看详情 ↗This is meta-llama/Meta-Llama-3-8B-Instruct with orthogonalized bfloat16 safetensor weights, generated with a refined methodology based on that which was described in the preview paper/blog post: 'Refusal in LLMs is mediated by a single direction' which I encourage you to read to understand more. TL;DR: This model has had certain weights manipulated to "inhibit" the model's ability to express refusal. It is not in anyway _guaranteed_ that it won't refuse you, understand your request, it may still lecture you about ethics/safety, etc. It is tuned in all other respects the same as the original 70B instruct model was, just with the strongest refusal directions orthogonalized out. **TL;TL;DR;DR: It's uncensored in the purest form I can manage -- no new or changed behaviour in any other respect from the original model.**
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查看详情 ↗暂未找到匹配模型
可以调整厂商、分类或输入输出条件；新模型正持续收录。
COST EXPLORER
好模型，也要算好成本。
调整月度 Token 用量，直观比较不同模型的预估 API 开支。
1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价
仅比较同一渠道的基础文本 Token 报价
使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。
MODEL HANDBOOK
读懂模型，再做选择
API 价格应该怎么比较？+
先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。
开放权重，就代表可以免费商用吗？+
不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。
排行榜第一，就是最适合我的模型吗？+
榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。